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Causal Inference in the Social Sciences

Annual Review of Statistics and Its Application, 2023
Knowledge of causal effects is of great importance to decision makers in a wide variety of settings. In many cases, however, these causal effects are not known to the decision makers and need to be estimated from data.
G. Imbens
semanticscholar   +1 more source

Computational Causal Inference

CoRR, 2020
We introduce computational causal inference as an interdisciplinary field across causal inference, algorithms design and numerical computing. The field aims to develop software specializing in causal inference that can analyze massive datasets with a variety of causal effects, in a performant, general, and robust way.
openaire   +2 more sources

Causal inference

Revista de la Facultad de Ciencias de la Salud, 2014
Establishing causality has been a problem throughout history of philosophy of science. This paper discusses the philosophy of causal inference along the different school of thoughts and methods: Rationalism, Empiricism, Inductive method, Hypothetical ...
Richard Shoemaker
doaj   +2 more sources

Causal Inference in Audiovisual Perception [PDF]

The Journal of Neuroscience, 2020
In our natural environment the senses are continuously flooded with a myriad of signals. To form a coherent representation of the world, the brain needs to integrate sensory signals arising from a common cause and segregate signals coming from separate causes.
Mihalik, Agoston, Noppeney, U., Noppeney, U.   +2 more
openaire   +5 more sources

y0-causal-inference/y0 v0.0.2

, 2021
Causal modeling in ...
Marc-Antoine Parent, Charles Tapley Hoyt
core   +1 more source

The role of causal inference in health services research II: a framework for causal inference. [PDF]

, 2020
In a previous Hints and Kinks, we discussed the role of causal inference in tasks of health services research (HSR) using examples from health system interventions (Moser et al. 2020).
Zwahlen, Marcel, Puhan, Milo A, Moser, André, Puhan, Milo A; https://orcid.org/   +3 more
core   +1 more source

Adverse events associated with COVID-19 vaccination or diagnosis among pregnant and non-pregnant women in the United States, 2021-2022

International Journal of Infectious Diseases
Objectives: To quantify the incidence of adverse events after COVID-19 vaccination and COVID-19 diagnosis in women of reproductive age; to examine pregnancy as a potential risk modifier.
Stacey L. Rowe, Sheena G. Sullivan, Julia J. Koerber, Flor M. Muñoz, Matthew M. Coates, Onyebuchi A. Arah, Annette K. Regan   +6 more
doaj   +1 more source

Bipartite Causal Inference with Interference

Statistical Science, 2021
Statistical methods to evaluate the effectiveness of interventions are increasingly challenged by the inherent interconnectedness of units. Specifically, a recent flurry of methods research has addressed the problem of interference between observations, which arises when one observational unit's outcome depends not only on its treatment but also the ...
Zigler, Corwin M., Papadogeorgou, Georgia   +1 more
openaire   +6 more sources

Estimating Mann–Whitney-Type Causal Effects for Right-Censored Survival Outcomes

Journal of Causal Inference, 2019
Mann–Whitney-type causal effects are clinically relevant, easy to interpret, and readily applicable to a wide range of study settings. This article considers estimation of such effects when the outcome variable is a survival time subject to right ...
Zhang Zhiwei, Liu Chunling, Ma Shujie, Zhang Min   +3 more
doaj   +1 more source

Inferring Causal Explanations [PDF]

, 1999
A popular approach to explanations amounts to backward chaining over logical implications encoding causal links. However, the resulting explanations are often unsatisfactory from a common-sense point of view. We define a framework allowing us to distinguish causal implication from mere logical implication.
Philippe Besnard, Marie-Odile Cordier
openaire   +1 more source